data:
  tokenizer: null
  train_files: ~/data/rlhf/gsm8k/train.parquet
  val_files: ~/data/rlhf/gsm8k/test.parquet
  prompt_key: prompt
  reward_fn_key: data_source
  max_prompt_length: 512
  max_response_length: 512
  train_batch_size: 1024
  val_batch_size: null
  return_raw_input_ids: False  # This should be set to true when the tokenizer between policy and rm differs
  return_raw_chat: False
  shuffle: True
  filter_overlong_prompts: False # for large-scale dataset, filtering overlong prompts could be timeconsuming. You cat set the filter_overlong_prompts_workers to use multiprocessing to speed up.
  filter_overlong_prompts_workers: 1
  truncation: error
  image_key: images
  video_key: videos
  custom_cls:
      path: null
      name: null

actor_rollout_ref:
  hybrid_engine: True
  model:
    path: ~/models/deepseek-llm-7b-chat
    external_lib: null
    override_config: { }
    enable_gradient_checkpointing: True
    use_remove_padding: False
    use_liger: False
  actor:
    strategy: fsdp  # This is for backward-compatibility
    ppo_mini_batch_size: 256
    ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
    ppo_micro_batch_size_per_gpu: null
    use_dynamic_bsz: False
    ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length}
    grad_clip: 1.0
    # pg_losses2 = -advantages * torch.clamp(ratio, 1 - cliprange_low, 1 + cliprange_high)
    clip_ratio: 0.2 # default value if clip_ratio_low and clip_ratio_high are not specified
    clip_ratio_low: 0.2
    clip_ratio_high: 0.2
    clip_ratio_c: 3.0 # lower bound of the value for Dual-clip PPO from https://arxiv.org/pdf/1912.09729
    loss_agg_mode: "token-mean" # / "seq-mean-token-sum" / "seq-mean-token-mean"
    entropy_coeff: 0
    use_kl_loss: False # True for GRPO
    use_torch_compile: True # False to disable torch compile
    kl_loss_coef: 0.001 # for grpo
    kl_loss_type: low_var_kl # for grpo
    ppo_epochs: 1
    shuffle: False
    ulysses_sequence_parallel_size: 1 # sp size
    checkpoint:
      contents: ['model', 'optimizer', 'extra']  # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space
    optim:
      lr: 1e-6
      lr_warmup_steps: -1 # Prioritized. Negative values mean delegating to lr_warmup_steps_ratio.
      lr_warmup_steps_ratio: 0.  # the total steps will be injected during runtime
      min_lr_ratio: null   # only useful for warmup with cosine
      warmup_style: constant  # select from constant/cosine
      total_training_steps: -1  # must be override by program
      weight_decay: 0.01
    fsdp_config:
      wrap_policy:
        # transformer_layer_cls_to_wrap: None
        min_num_params: 0
      param_offload: False
      optimizer_offload: False
      fsdp_size: -1
  ref:
    strategy: fsdp
    fsdp_config:
      param_offload: False
      wrap_policy:
        # transformer_layer_cls_to_wrap: None
        min_num_params: 0
    use_torch_compile: ${actor_rollout_ref.actor.use_torch_compile}
    log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
    log_prob_micro_batch_size_per_gpu: null
    log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
    log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}
    ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size
  rollout:
    name: vllm
    mode: sync # sync: LLM, async: AsyncLLM
    chat_scheduler: null # async chat scheduler, e.g examples.ppo_trainer.naive_chat_scheduler.NaiveChatCompletionScheduler
    temperature: 1.0
    top_k: -1 # 0 for hf rollout, -1 for vllm rollout
    top_p: 1
    use_fire_sampling: False # https://arxiv.org/abs/2410.21236
    prompt_length: ${data.max_prompt_length}  # not use for opensource
    response_length: ${data.max_response_length}
    # for vllm rollout
    dtype: bfloat16 # should align with FSDP
    gpu_memory_utilization: 0.5
    ignore_eos: False
    enforce_eager: True
    free_cache_engine: True
    load_format: dummy_dtensor
    tensor_model_parallel_size: 2
    max_num_batched_tokens: 8192
    max_model_len: null
    max_num_seqs: 1024
    log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
    log_prob_micro_batch_size_per_gpu: null
    log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
    log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}
    disable_log_stats: True
    enable_chunked_prefill: True # may get higher throughput when set to True. When activated, Please increase max_num_batched_tokens or decrease max_model_len.
    # for hf rollout
    do_sample: True
    # number of responses (i.e. num sample times)
    n: 1 # > 1 for grpo
    engine_kwargs: # inference engine parameters
      swap_space: null # null means "use the engine default value" (usually 4 GB), setting it to, e.g., 32 means 32 GB
    val_kwargs:
      # sampling parameters for validation
      top_k: -1 # 0 for hf rollout, -1 for vllm rollout
      top_p: 1.0
      temperature: 0
      n: 1
      do_sample: False # default eager for validation

critic:
  rollout_n: ${actor_rollout_ref.rollout.n}
  strategy: fsdp
  optim:
    lr: 1e-5
    lr_warmup_steps_ratio: 0.  # the total steps will be injected during runtime
    min_lr_ratio: null   # only useful for warmup with cosine
    warmup_style: constant  # select from constant/cosine
    total_training_steps: -1  # must be override by program
    weight_decay: 0.01
  model:
    path: ~/models/deepseek-llm-7b-chat
    tokenizer_path: ${actor_rollout_ref.model.path}
    override_config: { }
    external_lib: ${actor_rollout_ref.model.external_lib}
    enable_gradient_checkpointing: True
    use_remove_padding: False
    fsdp_config:
      param_offload: False
      optimizer_offload: False
      wrap_policy:
        # transformer_layer_cls_to_wrap: None
        min_num_params: 0
      fsdp_size: -1
  ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size}
  ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
  ppo_micro_batch_size_per_gpu: null
  forward_micro_batch_size: ${critic.ppo_micro_batch_size}
  forward_micro_batch_size_per_gpu: ${critic.ppo_micro_batch_size_per_gpu}
  use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
  ppo_max_token_len_per_gpu: 32768 # (${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}) * 2
  forward_max_token_len_per_gpu: ${critic.ppo_max_token_len_per_gpu}
  ulysses_sequence_parallel_size: 1 # sp size
  ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs}
  shuffle: ${actor_rollout_ref.actor.shuffle}
  grad_clip: 1.0
  cliprange_value: 0.5
  checkpoint:
    contents: ['model', 'optimizer', 'extra']  # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space

reward_model:
  enable: False
  strategy: fsdp
  model:
    input_tokenizer: ${actor_rollout_ref.model.path}  # set this to null if the chat template is identical
    path: ~/models/FsfairX-LLaMA3-RM-v0.1
    external_lib: ${actor_rollout_ref.model.external_lib}
    use_remove_padding: False
    fsdp_config:
      wrap_policy:
        min_num_params: 0
      param_offload: False
      fsdp_size: -1
  micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu
  micro_batch_size_per_gpu: null # set a number
  max_length: null
  ulysses_sequence_parallel_size: 1 # sp size
  use_dynamic_bsz: ${critic.use_dynamic_bsz}
  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
  reward_manager: naive
  launch_reward_fn_async: False # custom reward function executed async on CPU, during log_prob

custom_reward_function:
  path: null
  name: compute_score

algorithm:
  gamma: 1.0
  lam: 1.0
  adv_estimator: gae
  norm_adv_by_std_in_grpo: True
  use_kl_in_reward: False
  kl_penalty: kl  # how to estimate kl divergence
  kl_ctrl:
    type: fixed
    kl_coef: 0.001
    horizon: 10000
    target_kl: 0.1
  use_format_reward: False
  use_advantage_decomposition: False


trainer:
  balance_batch: True
  total_epochs: 30
  total_training_steps: null
  project_name: verl_examples
  experiment_name: gsm8k
  logger: [ 'console', 'wandb' ]
  log_val_generations: 0
  rollout_data_dir: null # directory for logging the rollout data, no dump if null
  validation_data_dir: null # directory for logging the validation data, no dump if null
  nnodes: 1
  n_gpus_per_node: 8
  save_freq: -1
  # auto: find the last ckpt to resume. If can't find, start from scratch
  resume_mode: auto # or disable or resume_path if resume_from_path is set
  resume_from_path: null
  val_before_train: True
  test_freq: -1
  critic_warmup: 0
  default_hdfs_dir: null
  del_local_ckpt_after_load: False
  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
  max_actor_ckpt_to_keep: null
  max_critic_ckpt_to_keep: null
  # The timeout for ray worker group to wait for the register center to be ready
  ray_wait_register_center_timeout: 300

ray_init:
  num_cpus: null # `None` means using all CPUs, which might cause hang if limited in systems like SLURM. Please set to a number allowed then.